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ESTIMATING THE DISTRIBUTION OF HARVESTED ESTUARINE BIVALVES WITH NATURAL-HISTORY-BASED HABITAT SUITABILITY MODELS.
Citation:
DeWitt, Ted, N. Lewis, AND EricW Fox. ESTIMATING THE DISTRIBUTION OF HARVESTED ESTUARINE BIVALVES WITH NATURAL-HISTORY-BASED HABITAT SUITABILITY MODELS. A Community on Ecosystem Services, Jacksonville, FL, December 05 - 09, 2016.
Impact/Purpose:
EPA scientists at NHEERL/WED have developed ecological models using readily obtained environmental data, that accurately generate maps of the best (e.g., “most suitable”) to worst habitats for locating populations of harvested species of bivalve shellfish within Oregon estuaries. Whereas these bivalves are valued in the recreational and commercial fisheries, and are thus an important final ecosystem good for coastal communities, maps of their distribution are useful to State and local decision-makers for planning development or uses of estuarine lands. The principle advantage of the new modeling approach is that disparate, independent sets of existing data were sufficient to produce and validate maps of habitat suitability. Additionally, by combining the models with forecasted changes in sea level and salinity, this modeling approach can be used to estimate changes in the distribution of harvested bivalves in response to climate change. The research was conducted as part of SHC Project 2.61, Task 3: Ecological Production Functions for Quantifying Final Ecosystem Goods and Services.
Description:
Habitat suitability models are used to forecast how environmental change may affect the abundance or distribution of species of interest. The development of habitat suitability models may be used to estimate the vulnerability of this valued ecosystem good to natural or anthropogenic stressors. Using natural history information, rule-based habitat suitability models were constructed in a GIS for two recreationally harvested bivalve species (cockles Clinocardium nuttallii; softshells Mya arenaria) common to NE Pacific estuaries (N. California to British Columbia). Tolerance limits of each species were evaluated with respect to four parameters that are easy to sample: salinity, depth, sediment grain size, and the presence of bioturbating burrowing shrimp and were determined through literature review. Spatially-explicit habitat maps were produced for Yaquina and Tillamook estuaries (Oregon) using environmental data from multiple studies ranging from 1960 to 2012. Suitability of a given location was ranked on a scale of 1-4 (lowest to highest) depending on the number of variables that fell within a bivalve’s tolerance limits. The models were tested by comparison of the distribution of each suitability class to the observed distribution of bivalves reported in benthic community studies (1996-2012). Results showed that the areas of highest habitat suitability (value=4) within our model contained the greatest proportion of bivalve observations and highest population densities, for both species. Our model was further supported by logistic regression analyses that showed correspondence between predicted habitat suitability values and logistic model probabilities. We demonstrate how these models can be used to forecast changes in the availability of suitable habitat for these species using projected changes in salinity and depth associated with climate scenarios for each estuary. The principle advantage of this approach is that disparate, independent sets of existing data were sufficient to parameterize the models, and to produce and validate maps of habitat suitability; however, not all estuaries have those data. Our next steps will be to test these models in other Pacific coast estuaries, and to apply this modeling approach for other harvested bivalve species . If these models are robust for multiple estuaries and bivalve species, fisheries resource managers will be able to transfer out approach to data-poor systems. Our habitat suitability models will be valuable tool to manage target species and require a relatively modest investment of time and money to collect the four rapidly-sampled environmental parameters.